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Risks 2016, 4(4), 36; doi:10.3390/risks4040036

Nested MC-Based Risk Measurement of Complex Portfolios: Acceleration and Energy Efficiency

1
Department of Mathematics, University of Kaiserslautern, 67663 Kaiserslautern, Germany
2
Microelectronic Systems Design Research Group, University of Kaiserslautern, 67663 Kaiserslautern, Germany
3
Department of Financial Mathematics, Fraunhofer ITWM, 67663 Kaiserslautern, Germany
*
Authors to whom correspondence should be addressed.
Academic Editor: Alexander Szimayer
Received: 23 June 2016 / Revised: 28 September 2016 / Accepted: 12 October 2016 / Published: 18 October 2016
(This article belongs to the Special Issue Applying Stochastic Models in Practice: Empirics and Numerics)
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Abstract

Risk analysis and management currently have a strong presence in financial institutions, where high performance and energy efficiency are key requirements for acceleration systems, especially when it comes to intraday analysis. In this regard, we approach the estimation of the widely-employed portfolio risk metrics value-at-risk (VaR) and conditional value-at-risk (cVaR) by means of nested Monte Carlo (MC) simulations. We do so by combining theory and software/hardware implementation. This allows us for the first time to investigate their performance on heterogeneous compute systems and across different compute platforms, namely central processing unit (CPU), many integrated core (MIC) architecture XeonPhi, graphics processing unit (GPU), and field-programmable gate array (FPGA). To this end, the OpenCL framework is employed to generate portable code, and the size of the simulations is scaled in order to evaluate variations in performance. Furthermore, we assess different parallelization schemes, and the targeted platforms are evaluated and compared in terms of runtime and energy efficiency. Our implementation also allowed us to derive a new algorithmic optimization regarding the generation of the required random number sequences. Moreover, we provide specific guidelines on how to properly handle these sequences in portable code, and on how to efficiently implement nested MC-based VaR and cVaR simulations on heterogeneous compute systems. View Full-Text
Keywords: Keywords nested MC simulation; value-at-risk; conditional value-at-risk; heterogeneous compute systems; OpenCL Keywords nested MC simulation; value-at-risk; conditional value-at-risk; heterogeneous compute systems; OpenCL
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Desmettre, S.; Korn, R.; Varela, J.A.; Wehn, N. Nested MC-Based Risk Measurement of Complex Portfolios: Acceleration and Energy Efficiency. Risks 2016, 4, 36.

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